Methods, systems, equipment, and media for determining the degree of weakening of passive soil strength in foundation pits.

Abstract

This invention relates to a method, system, equipment, and medium for determining the degree of weakening of passive soil strength in a foundation pit. The method includes: determining multiple target locations within the passive zone of the foundation pit and obtaining the first strength parameters of the soil at each target location before excavation; defining the weakened area within the passive zone, then obtaining and sampling multiple geological parameters within the weakened area to obtain multiple parameter samples; using a numerical analysis model to calculate the calculated deformation value corresponding to each parameter sample after excavation, thereby constructing a surrogate model; obtaining the actual deformation value of the support structure after excavation, using the surrogate model to obtain the geological parameters corresponding to the actual deformation value, and then using a finite element model to calculate the excavation of the foundation pit to obtain the second strength parameters of the soil at each target location after excavation; and obtaining the degree of soil weakening in the passive zone of the foundation pit after excavation based on the first and second strength parameters of each target location.

Description

Technical Field

[0001] This invention belongs to the field of foundation pit engineering technology, and in particular relates to a method, system, equipment and medium for determining the degree of weakening of passive soil strength in foundation pits. Background Technology

[0002] Accidents involving large deformations during foundation pit excavation are common. However, existing foundation pit monitoring standards are overly stringent in controlling foundation pit deformation, making it difficult to grasp the principles of deformation control during actual construction. This situation is particularly prominent in soft soil strata. The instability of the foundation pit support structure is closely related to the weakening of the soil strength in the passive zone after excavation. Current foundation pit monitoring generally only focuses on the displacement and stress of the foundation pit support structure, neglecting the fundamental factor causing foundation pit deformation: the weakening of soil strength. The analysis and monitoring of soil strength weakening are insufficient. Summary of the Invention

[0003] The purpose of this invention is to provide a method, system, equipment and medium for determining the degree of weakening of the passive soil strength in a foundation pit, and to determine the degree of weakening of the passive soil strength after the excavation of a soft soil foundation pit.

[0004] This invention is achieved through the following technical solution:

[0005] A method for determining the degree of weakening of passive soil strength in a foundation pit includes the following steps:

[0006] Multiple target locations were identified within the passive zone of the foundation pit, and the first strength parameters of the soil at each target location before the foundation pit excavation were obtained.

[0007] Within the passive zone of the foundation pit, the scope of weakening is defined and the weakening range is determined.

[0008] Multiple geological parameters within the weakened range are obtained, and the obtained geological parameters are sampled to obtain multiple parameter samples;

[0009] A numerical analysis model is established, and the calculated deformation value of the support structure after the foundation pit is excavated is calculated based on each parameter sample. The calculated deformation value corresponding to each parameter sample is obtained, and the parameter sample and its corresponding calculated deformation value are combined to form a training sample.

[0010] Construct a proxy model based on multiple training samples;

[0011] Obtain the actual deformation value of the support structure after the foundation pit is excavated, and use the surrogate model to obtain the geological parameters corresponding to the actual deformation value;

[0012] A finite element analysis model was established. Based on the geological parameters corresponding to the actual deformation values, the finite element model was used to calculate the foundation pit excavation and obtain the second strength parameters of the soil at each target location after the foundation pit excavation.

[0013] Based on the first and second strength parameters at each target location, the degree of soil weakening in the passive zone of the foundation pit after excavation is obtained.

[0014] Furthermore, multiple target locations are divided into multiple rows, and the distance between the multiple rows of target locations and the support structure is set from near to far, while the target locations in the same row are set at intervals from top to bottom;

[0015] The steps for determining the degree of soil weakening in the passive zone of the foundation pit after excavation, based on the first and second strength parameters at each target location, include:

[0016] The degree of soil weakening in the passive zone of the foundation pit after excavation is calculated using the following formula:

[0017] ;

[0018] In the formula, This represents the degree of soil weakening in the passive zone of the foundation pit after excavation. Let be the first intensity parameter of the j-th target position in the k-th row. The second intensity parameter is the position of the j-th target in the k-th row. Let be the number of target positions in the k-th row. This represents the weight of the soil strength in the kth row.

[0019] Furthermore, in the step based on multiple training samples, the objective function of the surrogate model is as follows:

[0020] ;

[0021] In the formula, and For Lagrange multipliers that appear in pairs, For new training samples, As training samples, For bias, K is the kernel function. l This represents the number of training samples.

[0022] Furthermore, the steps for obtaining the actual deformation value of the support structure after the foundation pit excavation and using a surrogate model to obtain the geological parameters corresponding to the actual deformation value include:

[0023] Obtain the actual deformation value of the support structure after the foundation pit is excavated;

[0024] Based on the actual deformation value of the support structure, the surrogate model is optimized using the Grey Wolf optimization algorithm to obtain the geological parameters corresponding to the actual deformation value.

[0025] Furthermore, the sampling steps for the acquired geological parameters include:

[0026] The Latin hypercube sampling method was used to sample multiple geological parameters after they were obtained.

[0027] Furthermore, the steps for obtaining the first strength parameters of the soil at each target location before the excavation of the foundation pit include:

[0028] Vane shear tests were performed at each target location to obtain the initial strength parameters of the soil at each target location before the excavation of the foundation pit.

[0029] This invention also provides a system for determining the degree of weakening of passive soil strength in foundation pits, comprising:

[0030] The first acquisition module is used to determine multiple target locations within the passive zone of the foundation pit and acquire the first strength parameters of the soil at each target location before the foundation pit is excavated.

[0031] The delineation module is used to define the scope of weakening within the passive zone of the foundation pit and determine the extent of weakening.

[0032] The sampling module is used to acquire multiple geological parameters within the weakened range and to sample these acquired geological parameters to obtain multiple parameter samples.

[0033] The first module is used to establish a numerical analysis model. Based on each parameter sample, the numerical analysis model calculates the calculated deformation value of the support structure after the foundation pit is excavated, obtains the calculated deformation value corresponding to each parameter sample, and combines the parameter sample and its corresponding calculated deformation value to form a training sample.

[0034] The construction module is used to construct a proxy model based on multiple training samples;

[0035] The second acquisition module is used to acquire the actual deformation value of the support structure after the foundation pit is excavated, and to obtain the geological parameters corresponding to the actual deformation value using the proxy model.

[0036] The second module is used to establish a finite element analysis model. Based on the geological parameters corresponding to the actual deformation values, the finite element model is used to calculate the excavation of the foundation pit and obtain the second strength parameters of the soil at each target location after the foundation pit is excavated.

[0037] The module is used to obtain the degree of soil weakening in the passive zone of the foundation pit after excavation, based on the first and second strength parameters at each target location.

[0038] The present invention also discloses an electronic device, which includes:

[0039] processor;

[0040] Memory is used to store executable computer programs;

[0041] Among them, the steps of determining the degree of weakening of the passive soil strength in the foundation pit are implemented when the processor executes the computer program.

[0042] The present invention also discloses a computer-readable storage medium storing a computer program thereon, wherein the computer program, when executed by a processor, implements the steps of a method for determining the degree of weakening of the passive soil strength in a foundation pit.

[0043] Compared with the prior art, the beneficial effects of the present invention are as follows: the first strength parameter of the soil on site is obtained before the foundation pit is excavated; during the foundation pit excavation process, the soil parameters are back-analyzed using numerical analysis and finite element analysis models based on the deformation value of the support structure, thereby obtaining the second strength parameter of the soil after the foundation pit is excavated; by comparing the second strength parameter with the first strength parameter, the degree of weakening of the soil strength under unloading and deformation conditions is obtained, thereby providing basic data for the stability of the foundation pit. Attached Figure Description

[0044] Figure 1 This is a schematic diagram illustrating the steps of the method for determining the degree of weakening of passive soil strength in foundation pits according to the present invention.

[0045] Figure 2 This is a schematic diagram of the foundation pit in the method for determining the degree of weakening of passive soil strength in the foundation pit according to the present invention.

[0046] In the diagram, 1-support structure, 2-target location, 3-ground surface, 4-bottom of the pit;

[0047] Figure 3 This is a schematic diagram of the modules of the system for determining the degree of weakening of passive soil strength in foundation pits according to the present invention.

[0048] Figure 4 This is a hardware structure diagram of the electronic device of the present invention. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0050] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0051] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0052] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0053] In the description of this invention, it should be noted that the terms "upper," "lower," "inner," "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship in which the product of this invention is usually placed when in use. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0054] Please see Figure 1 , Figure 1 This is a schematic diagram illustrating the steps of the method for determining the degree of weakening of passive soil strength in an excavation pit according to the present invention. A method for determining the degree of weakening of passive soil strength in an excavation pit includes the following steps:

[0055] S1. Determine multiple target locations within the passive zone of the foundation pit and obtain the first strength parameters of the soil at each target location before the foundation pit is excavated.

[0056] S2. Within the passive zone of the foundation pit, define the scope of weakening and determine the weakening range;

[0057] S3. Obtain multiple geological parameters within the weakened range, and sample the obtained multiple geological parameters to obtain multiple parameter samples;

[0058] S4. Establish a numerical analysis model. Using the numerical analysis model, calculate the calculated deformation value of the support structure after the foundation pit is excavated based on each parameter sample. Obtain the calculated deformation value corresponding to each parameter sample and combine the parameter sample and its corresponding calculated deformation value to form a training sample.

[0059] S5. Construct a proxy model based on multiple training samples;

[0060] S6. Obtain the actual deformation value of the support structure after the foundation pit is excavated, and use the proxy model to obtain the geological parameters corresponding to the actual deformation value.

[0061] S7. Establish a finite element analysis model. Based on the geological parameters corresponding to the actual deformation values, use the finite element model to calculate the foundation pit excavation and obtain the second strength parameters of the soil at each target location after the foundation pit excavation.

[0062] S8. Based on the first and second strength parameters at each target location, the degree of soil weakening in the passive zone of the foundation pit after excavation is obtained.

[0063] In step S1 above, before the excavation of the foundation pit, multiple target locations are determined at different depths and positions within the passive zone of the foundation pit. Specifically, these target locations are divided into multiple rows, with the distance between each row of target locations and the support structure arranged from near to far. Target locations within the same row are spaced out from top to bottom, with an interval of at least 0.5m. The distance from the lowest target location to the ground is H, and the horizontal width is 1.2~1.5H. That is, the horizontal distance between the row of target locations furthest from the support structure and the support structure is 1.2~1.5H. Specific locations are as follows... Figure 2 As shown. Then, before the foundation pit is excavated, vane shear tests are performed at each target location to obtain the first strength parameters of the soil at each target location before disturbance.

[0064] In step S2 above, based on the geological parameters of the passive zone of the foundation pit provided in the site survey report, the range of geological parameters of different types of soil has relatively clear boundaries. For example, the cohesion of sandy soil is basically within 5 degrees, and the friction angle is approximately in the range of 23 to 35 degrees. Therefore, the soil type can be determined based on the geological parameters, and then the range of possible weakening of the passive zone of the foundation pit can be defined and the weakening range determined. This is the existing technology and will not be elaborated here.

[0065] In step S3 above, the exploration report will test multiple soil samples for each type of soil to obtain the geological parameters of the soil. After defining the weakening range, multiple geological parameters within the weakening range can be obtained from the field exploration report. However, the weakened soil may exceed the range of these original geological parameters. Therefore, it is necessary to expand the range of geological parameters within a reasonable range. The expansion method is to use the Latin hypercube sampling method to sample the multiple geological parameters obtained, and the geological parameter variables are:

[0066] (1);

[0067] Where x represents a geological parameter, n This indicates the dimension of geological parameter variables (i.e., different geological parameters). l To determine the number of sample points, the value range of each geological parameter (such as unit weight, cohesion, friction angle, modulus, etc.) is divided into n continuous intervals. Random sampling is performed within each interval, and the sampled variable values ​​are randomly combined to obtain n parameter samples. The n parameter samples are shown below:

[0068] Sample 1 (weight 1, cohesion 1, friction angle 1, modulus 1, ...);

[0069] Sample 2 (weight 2, cohesion 2, friction angle 2, modulus 2, ...); ..............;

[0071] Sample n (specific gravity n, cohesion n, friction angle n, modulus n, ...).

[0072] In step S4 above, a numerical analysis model is established using finite element software. Using the n parameter samples obtained by random combination in step S3, the numerical analysis model calculates the deformation of the support structure of the foundation pit excavation based on each parameter sample, and obtains the calculated deformation value corresponding to each parameter sample. The parameter samples and the calculated deformation values ​​corresponding to each parameter sample are combined into training samples, so that multiple training samples are obtained based on multiple parameter samples and the calculated deformation values ​​corresponding to each parameter sample.

[0073] In step S5 above, a surrogate model is constructed using Support Vector Regression (SVR) based on multiple training samples. Considering the effects of random error and noise, the training samples are substituted into the surrogate model to solve for the surrogate model parameters. The function value can be calculated using the decision function in formula (2):

[0074] (2);

[0075] and They are Lagrange multipliers that appear in pairs (dual variables). For new training samples, The training samples are the parameter samples obtained in step S4 and the corresponding calculated deformation values. This is used as a bias. Considering the highly nonlinear relationship between the model response and soil parameter variables, a kernel function is introduced. K Mapping low-dimensional sample data to a high-dimensional feature space allows linearly inseparable data to be separable in the high dimension, using kernel functions. KThe radial basis kernel function is shown in formula (3):

[0076] (3);

[0077] In the formula, during the training phase, , All values ​​are training samples; during the prediction phase, For the new sample values, use replace, For training sample values, To connect the data points using the Euclidean distance, The width parameter controls the scope of the function.

[0078] During the solution process, since both numerical calculations and settlement observations have certain errors, in order to improve the generalization ability of the prediction model and consider the influence of random errors, an insensitive zone loss function is introduced into this model. ε Assuming that the distance of all samples to the regression function is less than 1 / 3 oz. ε At this point, solving the regression function (2) can be transformed into a convex quadratic optimization problem, namely:

[0079] (4);

[0080] The above equation must also satisfy the following constraints:

[0081]

[0082] In the formula, C is a positive constant called the penalty factor, and the relaxation factors ξ and ξ* correspond to the two cases where the sample data points are above and below the regression curve, respectively.

[0083] During the model training phase, the Lagrange multipliers are optimized using formula (4) to solve for the optimal value. and And satisfy the constraints, output the trained... , and This allows us to obtain a surrogate model that shows the relationship between trained geological parameters and the deformation values ​​of the support structure.

[0084] In step S6 above, during the excavation of the foundation pit, the support structure is monitored to obtain its actual deformation value. Then, the objective function of the constructed surrogate model is optimized to obtain geological parameters that match the actual deformation value. Finally, the true values ​​of the test samples can be used. and model predictions Calculate the total error The generalization ability of the model is verified as shown in formula (5):

[0085] (5);

[0086] In the formula, n The number of measured response data points. S i This represents the actual deformation value of the support structure. S i * This represents the calculated deformation value of the support structure.

[0087] Furthermore, the objective function of the constructed surrogate model can be optimized using existing optimization methods. Preferably, the gray wolf optimization algorithm is used to optimize the surrogate model to obtain geological parameters that match the actual deformation values.

[0088] In the gray wolf optimization algorithm, the gray wolf pack is divided into four levels:

[0089] Alpha wolf: The alpha wolf in a wolf pack, primarily responsible for decision-making, such as hunting, roosting, and resting times. In the algorithm, it represents the current optimal solution.

[0090] β wolf: A second-level wolf that obeys the α wolf and assists it in making decisions. In the algorithm, it represents a suboptimal solution.

[0091] δ wolf: A third-rank wolf, subordinate to α and β wolves, and dominating other lower-rank wolves. In the algorithm, it represents the third best solution.

[0092] ω-wolves: Fourth-rank wolves, who must obey all other higher-rank wolves. In the algorithm, they represent the remaining candidate solutions.

[0093] The mathematical model for the optimization behavior (circling hunting) is as follows:

[0094] (6);

[0095] (7);

[0096] Where t represents the current iteration number, A and C are coefficients, represents the prey position (i.e., the optimization objective), represents the position of the gray wolf individual in generation t, and A and C are parameters.

[0097] The specific implementation steps of the Grey Wolf optimization algorithm are as follows:

[0098] Step (1): Initialize the population parameters, including the population size N, the maximum number of iterations Maxiter, and the control parameters a, A, and C;

[0099] (8);

[0100] (9);

[0101] in, , It is a random value in [0,1]; in order to simulate approximating the prey (close to the optimization target value), it is a random number in the interval [-a,a], where a decreases from 2 to 0 during the iteration process;

[0102] Step (2): Randomly initialize the positions of individual gray wolves based on the upper and lower bounds of the variables. X ;

[0103] Step (3): Calculate the fitness value of each wolf, and save the position information of the wolves with the best, second best, and third best fitness values ​​in the population as the positions of α wolf, β wolf, and δ wolf, respectively.

[0104] (10);

[0105] (11);

[0106] (12);

[0107] in, , , These represent the distances between α wolf, β wolf, and δ wolf and other individual wolves, respectively. , , These represent the current positions of α wolf, β wolf, and δ wolf, respectively. , , It is a random number, determined by equation (9). X This is the current location of the individual gray wolf;

[0108] Step (4), update the individual gray wolf X The position is usually updated based on the position information of the alpha wolf, beta wolf, and delta wolf;

[0109] (13);

[0110] (14);

[0111] (15);

[0112] in, , , These represent the adjusted positions of the other wolves (ω wolves) after being influenced by α wolves, β wolves, and δ wolves, respectively. The average value is used here.

[0113] (16);

[0114] Step (5), update parameters a, A, and C;

[0115] Step (6): Calculate the fitness value of each gray wolf and update the optimal positions of α wolf, β wolf and δ wolf;

[0116] Step (7): Determine if the maximum number of iterations has been reached. If the maximum number of iterations has been reached, the algorithm stops and returns the optimal solution; otherwise, proceed to step (4) to continue iterating.

[0117] Step (8): Output the position of wolf α as the optimal solution.

[0118] In step S7 above, the geological parameters obtained in step S6 that match the actual deformation values ​​are used to establish a finite element analysis model using finite element software. The excavation of the foundation pit is analyzed and calculated to obtain the second strength parameters of the soil at each target location in the passive zone of the foundation pit after the excavation, as well as the distribution of the plastic zone of the soil in the passive zone of the foundation pit, so as to determine the range of influence of the degree of soil weakening.

[0119] In step S8 above, the degree of soil weakening in the passive zone of the foundation pit after excavation is calculated using the following formula:

[0120] ;

[0121] In the formula, This represents the degree of soil weakening in the passive zone of the foundation pit after excavation. Let be the first intensity parameter of the j-th target position in the k-th row. The second intensity parameter is the position of the j-th target in the k-th row. Let be the number of target positions in the k-th row. The weight for the strength of the k-th row of soil is determined by the principle that the weights closer to the support structure are taken as larger values, and the weights farther from the support structure are taken as smaller values. Further, the weights... The specific method for determining the value is as follows:

[0122] ;

[0123] In the formula, Let k be the distance between the strength point of the k-th cross plate and the last row furthest from the support structure, where k is taken as 1 from the point closest to the support structure.

[0124] Please see Figure 3 , Figure 3 This is a schematic diagram of the module of the system for determining the degree of weakening of passive soil strength in a foundation pit according to the present invention. Corresponding to the aforementioned embodiments of the method for determining the degree of weakening of passive soil strength in a foundation pit according to the present invention, the present invention also provides a system for determining the degree of weakening of passive soil strength in a foundation pit, comprising:

[0125] The first acquisition module 10 is used to determine multiple target locations within the passive zone of the foundation pit and acquire the first strength parameters of the soil at each target location before the foundation pit is excavated.

[0126] The delineation module 20 is used to delineate the scope of weakening within the passive zone of the foundation pit and determine the scope of weakening.

[0127] The sampling module 30 is used to acquire multiple geological parameters within the weakened range and to sample the acquired multiple geological parameters to obtain multiple parameter samples.

[0128] The first module 40 is used to establish a numerical analysis model. Based on each parameter sample, the numerical analysis model is used to calculate the calculated deformation value of the support structure after the foundation pit is excavated, obtain the calculated deformation value corresponding to each parameter sample, and combine the parameter sample and its corresponding calculated deformation value to form a training sample.

[0129] Module 50 is used to construct a proxy model based on multiple training samples;

[0130] The second acquisition module 60 is used to acquire the actual deformation value of the support structure after the foundation pit is excavated, and to obtain the geological parameters corresponding to the actual deformation value using the proxy model.

[0131] The second module 70 is used to establish a finite element analysis model. Based on the geological parameters corresponding to the actual deformation values, the finite element model is used to calculate the excavation of the foundation pit and obtain the second strength parameters of the soil at each target location after the foundation pit is excavated.

[0132] Module 80 is obtained to obtain the degree of soil weakening in the passive zone of the foundation pit after excavation, based on the first and second strength parameters at each target location.

[0133] Furthermore, multiple target locations are divided into multiple rows, and the distance between the multiple rows of target locations and the support structure is set from near to far, while the target locations in the same row are set at intervals from top to bottom;

[0134] Module 80 includes:

[0135] The calculation submodule is used to calculate the degree of soil weakening in the passive zone of the foundation pit after excavation using the following formula:

[0136] ;

[0137] In the formula, This represents the degree of soil weakening in the passive zone of the foundation pit after excavation. Let be the first intensity parameter of the j-th target position in the k-th row. The second intensity parameter is the position of the j-th target in the k-th row. Let be the number of target positions in the k-th row. This represents the weight of the soil strength in the kth row.

[0138] Furthermore, the second acquisition module 60 includes:

[0139] The acquisition submodule is used to acquire the actual deformation value of the support structure after the foundation pit is excavated;

[0140] The optimization submodule is used to optimize the surrogate model based on the actual deformation value of the support structure using the Grey Wolf optimization algorithm to obtain the geological parameters corresponding to the actual deformation value.

[0141] Furthermore, the sampling module 30 includes:

[0142] The sampling module is used to sample multiple geological parameters after acquisition using the Latin hypercube sampling method.

[0143] Furthermore, the first acquisition module 10 includes:

[0144] The testing submodule is used to perform vane shear tests at each target location to obtain the initial strength parameters of the soil at each target location before the foundation pit is excavated.

[0145] The implementation process of the functions and roles of each module and submodule in the above system is detailed in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0146] For the system embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The system embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units.

[0147] Corresponding to the embodiments of the aforementioned method for determining the degree of weakening of passive soil strength in foundation pits, the present invention also provides an electronic device, which may include: a processor; a memory for storing an executable computer program; wherein, when the processor executes the computer program, it implements the method for determining the degree of weakening of passive soil strength in foundation pits in any of the aforementioned method embodiments.

[0148] The embodiments of the method and system for determining the degree of weakening of passive soil strength in foundation pits provided in this invention can all be applied to electronic devices. Taking software implementation as an example, as a logical device, it is formed by the processor of the electronic device loading the corresponding computer program instructions from non-volatile memory into memory for execution. From a hardware perspective, such as... Figure 4 As shown, except Figure 4In addition to the processor, memory, network interface, and non-volatile memory shown, the electronic device may also include other hardware, such as a camera module; or, depending on the actual function of the electronic device, it may also include other hardware, which will not be elaborated further.

[0149] Corresponding to the aforementioned embodiments of the method for determining the degree of weakening of passive soil strength in foundation pits, this embodiment of the invention also provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements the method for determining the degree of weakening of passive soil strength in foundation pits in any of the aforementioned method embodiments.

[0150] Embodiments of the present invention may take the form of a computer program product implemented on one or more storage media containing program code (including but not limited to disk storage, CD-ROM, optical storage, etc.). The computer-readable storage medium may include: permanent or non-permanent removable or non-removable media. The information storage function of the computer-readable storage medium can be implemented by any feasible method or technology. The information may be computer-readable instructions, data structures, program models, or other data.

[0151] In addition, computer-readable storage media include, but are not limited to: phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or other non-transfer media that can be used to store information accessible by a computing device.

[0152] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Therefore, any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for determining the degree of weakening of passive soil strength in a foundation pit, characterized in that, Includes the following steps: Multiple target locations are determined within the passive zone of the foundation pit, and the first strength parameters of the soil at each target location before the foundation pit is excavated are obtained. The multiple target locations are divided into multiple rows, and the distance between the target locations in the multiple rows and the support structure is set from near to far. The target locations in the same row are set at intervals from top to bottom. Within the passive zone of the foundation pit, the scope of weakening is defined and the weakening range is determined. Multiple geological parameters within the weakened range are obtained, and the obtained geological parameters are sampled to obtain multiple parameter samples; A numerical analysis model is established, and the calculated deformation value of the support structure after the foundation pit is excavated is calculated based on each parameter sample using the numerical analysis model. The calculated deformation value corresponding to each parameter sample is obtained, and the parameter sample and its corresponding calculated deformation value are combined to form a training sample. Based on multiple training samples, a proxy model is constructed. The objective function of the proxy model is as follows: ； In the formula, and For Lagrange multipliers that appear in pairs, For new training samples, As training samples, For bias, K For kernel function, l This represents the number of training samples; Obtain the actual deformation value of the support structure after the foundation pit is excavated, and use the surrogate model to obtain the geological parameters corresponding to the actual deformation value; A finite element analysis model is established. Based on the geological parameters corresponding to the actual deformation values, the finite element model is used to calculate the foundation pit excavation and obtain the second strength parameters of the soil at each target location after the foundation pit excavation. Based on the first and second strength parameters of each target location, the degree of soil weakening in the passive zone of the foundation pit after excavation is obtained. The step of obtaining the degree of soil weakening in the passive zone of the foundation pit after excavation based on the first and second strength parameters at each of the target locations includes: The degree of soil weakening in the passive zone of the foundation pit after excavation is calculated using the following formula: ； In the formula, This represents the degree of soil weakening in the passive zone of the foundation pit after excavation. Let be the first intensity parameter of the j-th target position in the k-th row. The second intensity parameter is the position of the j-th target in the k-th row. Let be the number of target positions in the k-th row. This represents the weight of the soil strength in the kth row.

2. The method for determining the degree of weakening of passive soil strength in a foundation pit according to claim 1, characterized in that, The steps of obtaining the actual deformation value of the support structure after foundation pit excavation and using the surrogate model to obtain the geological parameters corresponding to the actual deformation value include: Obtain the actual deformation value of the support structure after the foundation pit is excavated; Based on the actual deformation value of the support structure, the surrogate model is optimized using the Grey Wolf optimization algorithm to obtain the geological parameters corresponding to the actual deformation value.

3. The method for determining the degree of weakening of passive soil strength in a foundation pit according to claim 1, characterized in that, The step of sampling the acquired geological parameters includes: The Latin hypercube sampling method was used to sample multiple geological parameters after they were obtained.

4. The method for determining the degree of weakening of passive soil strength in a foundation pit according to claim 1, characterized in that, The step of obtaining the first strength parameters of the soil at each of the target locations before the excavation of the foundation pit includes: A vane shear test was performed at each of the target locations to obtain the first strength parameters of the soil at each target location before the excavation of the foundation pit.

5. A system for determining the degree of weakening of passive soil strength in foundation pits, characterized in that, include: The first acquisition module is used to determine multiple target locations within the passive zone of the foundation pit and acquire the first strength parameters of the soil at each target location before the foundation pit is excavated. The target locations are divided into multiple rows, and the distance between the target locations in the multiple rows and the support structure is set from near to far. The target locations in the same row are set at intervals from top to bottom. The delineation module is used to define the scope of weakening within the passive zone of the foundation pit and determine the extent of weakening. The sampling module is used to acquire multiple geological parameters within the weakened range and to sample the acquired multiple geological parameters to obtain multiple parameter samples. The first module is used to establish a numerical analysis model. Based on each parameter sample, the numerical analysis model is used to calculate the calculated deformation value of the support structure after the foundation pit is excavated, obtain the calculated deformation value corresponding to each parameter sample, and combine the parameter sample and its corresponding calculated deformation value to form a training sample. The construction module is used to construct a proxy model based on multiple training samples. The objective function of the proxy model is as follows: ； In the formula, and For Lagrange multipliers that appear in pairs, For new training samples, As training samples, For bias, K For kernel function, l This represents the number of training samples; The second acquisition module is used to acquire the actual deformation value of the support structure after the foundation pit is excavated, and to obtain the geological parameters corresponding to the actual deformation value using the proxy model. The second module is used to establish a finite element analysis model. Based on the geological parameters corresponding to the actual deformation values, the finite element model is used to calculate the foundation pit excavation and obtain the second strength parameters of the soil at each target location after the foundation pit excavation. The module is used to obtain the degree of soil weakening in the passive zone of the foundation pit after excavation, based on the first strength parameter and the second strength parameter of each of the target locations. The module obtained includes: The calculation submodule is used to calculate the degree of soil weakening in the passive zone of the foundation pit after excavation using the following formula: ； In the formula, This represents the degree of soil weakening in the passive zone of the foundation pit after excavation. Let be the first intensity parameter of the j-th target position in the k-th row. The second intensity parameter is the position of the j-th target in the k-th row. Let be the number of target positions in the k-th row. This represents the weight of the soil strength in the kth row.

6. An electronic device, characterized in that, include: processor; Memory is used to store executable computer programs; Wherein, when the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-4.

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